Discrimination of Boletaceae mushrooms based on data fusion of FT-IR and ICP-AES combined with SVM

文献类型： 外文期刊

第一作者： Yao, Sen

作者： Yao, Sen;Li, JieQing;Liu, HongGao;Wang, YuanZhong;Yao, Sen;Wang, YuanZhong;Li, Tao

作者机构：

关键词： Boletaceae mushrooms; Data fusion; Discrimination; Quality control; Support vector machine (SVM)

期刊名称：INTERNATIONAL JOURNAL OF FOOD PROPERTIES （ 影响因子：2.727； 五年影响因子：2.938 ）

ISSN： 1094-2912

年卷期： 2018 年 21 卷 1 期

页码：

收录情况： SCI

摘要： In this study, the individual and data fusion of Fourier transform infrared (FT-IR) spectroscopy and inductively coupled plasma atomic emission spectrometry (ICP-AES) were used for the discrimination of five species of Boletaceae mushrooms with the aid of support vector machine (SVM). First, the original FT-IR spectra of 230 samples with different species were preprocessed and optimized by second derivative (2D), Savitzky-Golay filter (15:1) and standardized normal variate. Second, the datasets of FT-IR spectra and ICP-AES were integrated, and the low-level data fusion strategy was used to classify different species mushrooms. Third, the latent variables of elements concentration and FT-IR spectra were extracted by partial least square discriminant analysis and two datasets were fused into a new matrix. Finally, the classification models were established by SVM. Compared with single spectroscopic technique, the mid-level data fusion strategy can provide better result. Especially, the accuracy of correct classification of samples in calibration and test sets were 100.00% and 98.68%, respectively. The results demonstrated that the mid-level data fusion of FT-IR and ICP-AES can provide higher synergic effect for the discrimination of different species Boletaceae mushrooms, which could be benefited for the further authentication and quality control of edible mushrooms.

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